fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python
By Daniel Müllner. 2013
663 citations recorded by the source corpus.
The fastcluster package is a C++ library for hierarchical, agglomerative clustering. It provides a fast implementation of the most efficient, current algorithms when the input is a dissimilarity index. Moreover, it features memory-saving routines for hierarchical clustering of vector data. It improves both asymptotic time complexity (in most cases) and practical performance (in all cases) compared to the existing implementations in standard software: several R packages, MATLAB, Mathematica, Python with SciPy.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/2f83ce70eda54ecdcec69cc0a98e6b8863f606ff
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dimitri Kartsaklis
- Mehrnoosh Sadrzadeh
- James Robertson
- John H. E. Nash
- Vijay S. Pande
- Lian Duan
- Lida Xu
- Brooke E. Husic
In Signal Processing
- James Robertson
- John H. E. Nash
- Mehrnoosh Sadrzadeh
- Vijay S. Pande
- Lian Duan
- Dimitri Kartsaklis
- Lida Xu
- Brooke E. Husic
Citations in the corpus
- Big data for cyber physical systems in industry 4.0: a survey (2019, methodology): “Typically, hierarchical methods iteratively group the previously generated clusters to form a hierarchical structure (Murtagh and Legendre 2014; Müllner 2013).”
- Prior Disambiguation of Word Tensors for Constructing Sentence Vectors (2013, methodology): “Implementation details Our code is mainly written in Python and C++, and for the actual clustering step we use the Python interface of the efficient FASTCLUSTER library (Müllner, 2013).”
- VIRIDIC—A Novel Tool to Calculate the Intergenomic Similarities of Prokaryote-Infecting Viruses (2020, neutral): “html) (Müllner 2013).”
- MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies (2018, positive): “Single-linkage clustering is performed using the fcluster [24] package from SciPy at two default distance thresholds (0.”
- Explaining individual predictions when features are dependent: More accurate approximations to Shapley values (2019, neutral): “We then do hierarchical, agglomerative clustering with the agglomeration method commonly denoted as “complete” (Müllner, 2013) and D in (17) as dissimilarity matrix.”
- The Molecular Signatures Database (MSigDB) hallmark gene set collection (2015, neutral): “We used agglomerative hierarchical clustering with average linkage as implemented in the fastclust R package (Müllner, 2013).”
- Unsupervised learning of dynamical and molecular similarity using variance minimization (2017, methodology): “Ward’s method is usually implemented according to the following recursive distance update [Müllner, 2013], where the clusters s and t have just been merged to create a new cluster, u , and the new distance between u and some other cluster v needs to be updated.”